Downlink (DL) transmission methods rely on knowledge of the channel at the transmitting base station (BS), or, more precisely, the availability of estimates of the channels between the BS antennas and the UTs to which this BS is transmitting information. This channel state information is then used to “precode” the information intended for each of the UTs prior to transmission, in such a way, that each of the UTs is able to decode the signals of its own interest.
The necessary channel state information is obtained by transmitting pilots (i.e., known signature waveforms) over the wireless medium and estimating these channels based on the received waveforms. Then these estimates are used for generating the MU-MIMO precoder (i.e., the transmission method) and for transmitting data to the UTs. Since the channels change over time (and frequency), the process of training is repeated periodically across the network. In what are referred to as “pilot-on-pilot” schemes, the pilot transmission cycles are aligned in time across all BSs, while in pilot-on-data schemes the pilot transmission cycles of a given BS overlap with data transmissions from other BSs. The fraction of time (or time-frequency slots) allocated to a single training session dictates the number of channel uses allocated for pilot training to each BS. The dimensionality of this “pilot-training” signal space places a constraint on the number of possible orthogonal (or, linearly independent) pilots that can be signaled during each training phase. Given that the number of BS-UT channels that need to be obtained across the whole network is well beyond the channel uses allocated for training in each training cycle, or equivalently well beyond the number of allotted signal space dimensions for pilot training, pilots have to be reused across the network.
There are two classes of training methods used in DL MU-MIMO for obtaining channel estimates at the transmitting BS. The two classes are effectively distinguished by the parties that transmit the pilots. In FDD-based training schemes, to estimate the channels between the BS and each of the UTs, pilots are first transmitted by the BS. Each UT then collects measurements of the transmitted pilots and estimates its own channels. Then over a shared channel on another band, the UTs communicate (feed back) these estimates to the BS in their cell.
In a second class of training schemes, referred to herein as TDD-based training schemes, estimates of the channels from each UT to its transmitting BS are obtained directly at the BS, by transmitting pilots from each of the UTs. These schemes rely on the notion of “channel reciprocity,” which states that the channel from a BS to a UT on a given band and at a given time instance is the same as, or more accurately, correlated to the channel from the UT to the BS on a possibly different band and at a possibly different time instance, provided the gaps in time-instances and frequency-bands of the two channels are sufficiently small, i.e., within the channel coherence time and bandwidth, respectively. These schemes rely on sending pilots in the uplink from a set of UTs, collecting measurements at the BS, estimating the BS-UT channels based on these measurements at the BS, and then performing MU-MIMO transmission from the BS to the UTs over the same band and within the channel coherence time.
Reducing the spatial reuse factor of the pilots reduces the number of pilots that need to be signaled within each training cycle. It thus reduces the pilot overhead and allows more slots to be used for data transmission. However, the need for reusing pilots spatially in all these training schemes comes at a cost in channel estimate quality. Consider estimating a channel between a BS and a UT in its cell by use of a given pilot (from a set of orthogonal pilots). UTs throughout the network using the same pilot interfere or “contaminate” the estimates between the BS and UT of interest. Typically, the closest the interfering UT is to the BS of interest, the largest the “pilot contamination” levels. As a result, although reducing the pilot spatial reuse factor increases the fraction of time dedicated to data transmission, it also increases the levels of pilot contamination and thus reduces the efficiency of the data transmission cycle.
An important issue in TDD-based training schemes is the fact that the pilot contamination caused by the reuse of a pilot in a neighboring cell strongly depends on which UT has been assigned the same pilot in the neighboring cells. Specifically, the quality of the estimate of the channel between a BS and a UT in its cell, obtained by a TDD-based training scheme, depends on the following quantities:                The large-scale power attenuation affecting transmissions from the UT to the BS, and the effective pilot transmit power; these dictate the power of the “useful pilot signal” component in the measured signal that is used for channel estimation at the BS of interest;        The large-scale power attenuation affecting transmissions from the UTs re-using the same pilot (in neighboring cells) to the BS of interest, and the associated transmit powers in these pilots; these dictate the power of the interference or pilot-contamination signal component in the measured signal that is used for channel estimation at the BS of interest.        
The value of any such large-scale power attenuation quantity is affected by several factors, including distance between the transmitting and receiving parties, shadowing, and other environmental factors. Knowing these quantities would allow the BS to optimally use the measurements in forming its channel estimate so as to maximize the estimate quality.
Consider the basic setting whereby any given cell assigns its pilots randomly to its UTs within the cell, and independently of other cell pilot assignments. In such a setting, the pilot transmitted by any one of the UTs experiences pilot contamination whose power may take values over a possibly wide range, depending on the locations (and the transmit powers) of the UTs re-using this pilot in neighboring cells. The BS may not in general possess knowledge of the interference level experienced by each of the pilots used by its UTs. In that case, it would have to be conservative in forming its estimates (i.e., it would have to assume the highest possible level or a very high level among the possible interference levels). This can result in a significant reduction of the channel estimate quality.
As a result, there is evidently potential for improving the performance of TDD-based training schemes for DL MU-MIMO cellular and other multi-site deployments.